Downloads · 30 days
3
1% of all-time downloads
bloom-sentence-correction
Jayveersinh-Raj/bloom-sentence-correction
bloom-sentence-correction is a machine learning model from Jayveersinh-Raj. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
Low Rank Adapter for Bloom decoder for grammar correction.
All-time downloads
204
Public
Repo size
29.5 MB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.bin9.9 MB · 50%
At a glance
- Library
- peft
- Access
- Public
- Created
- Jun 27, 2023
- Updated
- Mar 5, 2024
- SHA
- b5a7cfbe
Base models
Model card
From the Hugging Face model README
Low Rank Adapter for Bloom decoder for grammar correction.
Example Usage:
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
from IPython.display import display, Markdown
peft_model_id = "Jayveersinh-Raj/bloom-sentence-correction"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
qa_model = PeftModel.from_pretrained(model, peft_model_id)
def make_inference(question):
batch = tokenizer(f"### INCORRECT\n{question}\n\n### CORRECT\n", return_tensors='pt').to("cuda")
with torch.cuda.amp.autocast():
output_tokens = qa_model.generate(**batch, max_new_tokens=200)
display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))
text = "I red a book last night"
make_inference(text)
- Library
- peft
- Created
- Jun 27, 2023
- Updated
- Mar 5, 2024